BIC: Collaborative Research: Evolutionary Optimization of Biological Circuits: Towards Cellular Programming

BIC：合作研究：生物回路的进化优化：迈向细胞编程

• 批准号: 0522831
• 负责人: Frances Arnold
• 金额: --
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0522831&HistoricalAwards=false
• 关键词: BIC; Collaborative; Research; Evolutionary; Optimization

The ability to redesign and even build completely new biological entities offers revolutionary opportunities for using biology to solve human problems. The dream, however, far outstrips the reality: engineering new biological machines, programming cells behaviors, and building new forms of life pose huge technical and conceptual challenges. Our research is designed to make some important first steps, by developing generally-applicable engineering methods for building synthetic gene networks. Implementation of even the most simple circuits in a biological system requires tedious optimization of a large number of poorly-understood parameters, many of which can neither be measured nor easily manipulated. Simulations can sometimes guide optimization, but we believe that biological systems are best optimized using nature's editing strategy, evolution. We believe that a combined approach of rational design based on computational predictions coupled with directed evolution-making mutations in the laboratory and selecting those organisms exhibiting the desired behaviors--will be fundamental to the progress of synthetic biology. We will in effect learn how to 'breed' useful synthetic gene networks, just as we have learned how to breed useful plants and animals.This approach mimics natural evolution in exploring the vast and complex landscape of functions available to a set of molecules making up an engineered regulatory pathway. Importantly, it circumvents our near-complete ignorance of how a DNA sequence encodes a specific set of biological functions, a detailed understanding that is required for any 'rational' design approach. By introducing random mutations into the DNA and screening for different functions that might be expressed by the mutant circuits, we can identify which functions are possible as well as the ranges of function available to the specific search process (e.g. random point mutation targeted to a specific gene). With further analysis, e.g. sequencing to identify the mutations and biochemical analysis of circuit components, we gain insights into the molecular mechanisms by which the overall function is achieved or modified. In this project we have three specific aims. The first is to validate a 'selection module' by which we can efficiently evolve components and circuits in the laboratory. This module connects proper circuit function to the ability of cells that express it to survive and grow. Cells with functioning circuits survive and grow; those that have not solved the problem do not. To program complex behaviors, we will also need components that respond to predefined ranges of input parameters with predictable output parameters. Thus our second aim is to use directed evolution to create a range of transcriptional activators based on the well-characterized framework protein, LuxR. Laboratory-evolved LuxR variants will activate gene transcription at different, nonnatural promoter sites on the DNA. Finally, we propose to investigate the range of circuit functions available to a predefined set of components via evolutionary exploration. Specifically, we will evolve a series of 'band detect' circuits that respond to a prespecified range of acyl-HSL concentrations. These circuits will be used to construct synthetic systems that form patterns of gene expression in the solid phase.Our ultimate goal is to develop a fundamental enabling technology for synthetic biology as well as for developing bio-inspired modes and architectures for computing. We envision that evolved circuits and the synthetic multicellular systems that can be constructed from them will be useful to researchers developing quantitative models of gene regulation, quorum sensing, and other aspects of cellular computing.

重新设计甚至构建全新生物实体的能力为利用生物学解决人类问题提供了革命性的机会。然而，这个梦想远远超出了现实：设计新的生物机器、编程细胞行为和构建新的生命形式带来了巨大的技术和概念挑战。 我们的研究旨在通过开发用于构建合成基因网络的通用工程方法来迈出重要的第一步。在生物系统中实现即使是最简单的电路也需要对大量不太了解的参数进行繁琐的优化，其中许多参数既不能测量也不容易操纵。模拟有时可以指导优化，但我们认为，生物系统最好使用自然的编辑策略，进化。我们相信，基于计算预测的合理设计与定向进化相结合的方法-在实验室中进行突变并选择那些表现出所需行为的生物体-将是合成生物学进步的基础。 实际上，我们将学习如何“培育”有用的合成基因网络，就像我们已经学会如何培育有用的植物和动物一样，这种方法模仿自然进化，探索构成工程调控途径的一组分子所具有的巨大而复杂的功能。 重要的是，它避免了我们对DNA序列如何编码一组特定生物功能的近乎完全的无知，这是任何“理性”设计方法所必需的详细理解。 通过在DNA中引入随机突变并筛选突变电路可能表达的不同功能，我们可以确定哪些功能是可能的，以及特定搜索过程可用的功能范围（例如针对特定基因的随机点突变）。 通过进一步的分析，例如测序以识别电路组件的突变和生化分析，我们可以深入了解实现或修改整体功能的分子机制。在这个项目中，我们有三个具体目标。 首先是验证一个“选择模块”，通过它我们可以在实验室中有效地进化元件和电路。这个模块将适当的电路功能与表达它的细胞生存和生长的能力联系起来。 有功能回路的细胞存活并生长;那些没有解决问题的细胞则不能。为了对复杂的行为进行编程，我们还需要能够对预定义的输入参数范围做出响应并具有可预测的输出参数的组件。因此，我们的第二个目标是使用定向进化来创建一系列基于良好表征的框架蛋白LuxR的转录激活因子。 进化的LuxR变体将在DNA上不同的非天然启动子位点激活基因转录。最后，我们建议通过进化探索来研究预定义组件集可用的电路功能范围。 具体来说，我们将发展一系列的“带检测”电路，响应预先指定的范围内的酰基-HSL浓度。 这些电路将用于构建合成系统，在固相中形成基因表达模式。我们的最终目标是为合成生物学开发一种基本的使能技术，并为计算开发生物启发的模式和架构。我们设想，进化的电路和合成的多细胞系统，可以从它们构建将是有用的研究人员开发定量模型的基因调控，群体感应，和其他方面的细胞计算。